Dynamic magnetic resonance imaging (MRI) involves creating a sequence of magnetic resonance (MR) images to monitor temporal changes in an object of interest (e.g., tissue structure). Dynamic MRI apparatus seek to acquire images as fast as possible while maintaining a sufficient signal to noise ratio (SNR) to investigate the object being imaged. Thus, image acquisition acceleration techniques may be employed. In one example, a reference data set may be created by under-sampling k-space and recording images simultaneously from multiple imaging coils. For example, partial parallel acquisition (PPA) strategies (e.g., sensitivity encoding (SENSE), generalized auto-calibrating partially parallel acquisition (GRAPPA)) facilitate accelerating image acquisition and are therefore employed in dynamic MRI.
Time-adaptive SENSE (TSENSE) and temporal GRAPPA (TGRAPPA) have also been employed in dynamic MRI. Both TSENSE and TGRAPPA are based on a time-interleaved phase encoding scheme. Conventionally, to support achieving an acceleration factor of R, raw data from R or more under-sampled time frames (e.g., frames that do not satisfy the Nyquist criteria) are assembled to obtain a reference data set that does satisfy the Nyquist criteria. The reference data set is used to calculate parameters (e.g., weights, coil sensitivity profiles) used in parallel image reconstruction. These parameters can then be used to reconstruct individual under sampled time frames. It may take a long time to acquire the reference data set due, for example, to long dynamic frames. Therefore the reference data set may be corrupted due, for example, to motion that occurs during acquisition.